Multi-objective Model Optimization for Inferring Gene Regulatory Networks

Spieth, Christian; Streichert, Felix; Speer, N.; Zell, Andreas

doi:10.1007/978-3-540-31880-4_42

Cited by 16 publications

(8 citation statements)

References 19 publications

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“…In GRN inference, we may additionally use MOO as a means to integrate topological constraints and prior knowledge, which we view as forms of proxy objectives. Some preliminary work [86], [15], [87] has exemplified these uses of MOO for GRN inference and the results obtained seem highly promising.…”

Section: Gene Regulatory Networkmentioning

confidence: 99%

Multiobjective Optimization in Bioinformatics and Computational Biology

Handl

Kell

Knowles

2007

IEEE/ACM Trans. Comput. Biol. and Bioinf.

273

131

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Abstract-This paper reviews the application of multiobjective optimization in the fields of bioinformatics and computational biology. A survey of existing work, organized by application area, forms the main body of the review, following an introduction to the key concepts in multiobjective optimization. An original contribution of the review is the identification of five distinct "contexts," giving rise to multiple objectives: These are used to explain the reasons behind the use of multiobjective optimization in each application area and also to point the way to potential future uses of the technique.

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Section: Gene Regulatory Networkmentioning

confidence: 99%

Multiobjective Optimization in Bioinformatics and Computational Biology

Handl

Kell

Knowles

2007

IEEE/ACM Trans. Comput. Biol. and Bioinf.

273

131

View full text Add to dashboard Cite

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“…Spieth and collaborators developed novel optimization methods to evolve the topology of a gene regulatory network as well as its parameter values, based on microarray data [348][349][350][351][352][353]; see also [354]. Liao's group combined S-system concepts with the method of network component analysis (NCA) for the estimation of transcription factor activity [355].…”

Section: �� T�or� ��Co�str�ctio� A�� St�� Ti�catio�mentioning

confidence: 99%

Biochemical Systems Theory: A Review

Voit

2013

ISRN Biomathematics

149

154

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Biochemical systems theory (BST) is the foundation for a set of analytical andmodeling tools that facilitate the analysis of dynamic biological systems. is paper depicts major developments in BST up to the current state of the art in 2012. It discusses its rationale, describes the typical strategies and methods of designing, diagnosing, analyzing, and utilizing BST models, and reviews areas of application. e paper is intended as a guide for investigators entering the fascinating �eld of biological systems analysis and as a resource for practitioners and experts. PreambleBiochemical systems theory (BST) is a mathematical and computational framework for analyzing and simulating systems. It was originally developed for biochemical pathways but by now has become much more widely applied to systems throughout biology and beyond. BST is called "canonical, " which means that model construction, diagnosis, and analysis follow stringent rules, which will be discussed throughout this paper. e key ingredient of BST is the power-law representation of all processes in a system.BST has been the focus of a number of books [1-6], including some in Chinese and Japanese [7][8][9]. e most detailed modern text dedicated speci�cally to BST is [3]. Moreover, since its inception, numerous reviews have portrayed the evolving state of the art in BST. Most of these reviews summarized methodological advances for the analysis of biochemical pathway systems . Others compared BST models with alternative modeling frameworks [32,[51][52][53][54][55][56][57][58][59][60][61][62][63]; some of these comparisons will be discussed later. Yet other reviews focused on specialty areas, such as customized methods for optimizing BST models (e.g., [64][65][66][67]) or estimating their parameters (e.g., [68][69][70][71][72]), strategies for discovering design and operating principles in natural system (e.g., [73][74][75][76][77][78]), and even the use of BST models in statistics [79][80][81]. A historical account of the �rst twenty years of BST was presented in [82].Supporting the methodological developments in the �eld, several soware packages were developed for different aspects of BST analysis. e earliest was ESSYNS [83][84][85], which supported all standard steady-state analyses and also contained a numerical solver that, at the time, was many times faster than any off-the-shelf soware [86,87]; see also [88]. Utilizing advances in computing and an extension of the solver from ESSYNS, Ferreira created the very user-friendly package PLAS, which is openly available and still very widely used [89] (see also [3]). Yamada and colleagues developed soware to translate E-cell models into BST models in PLAS format [90]. Okamoto's group created the soware BestKit, which permits the graphical design and translation of models, along with their analysis [91][92][93]. Vera and colleagues developed the soware tool PLMaddon for analyzing BST models within SBML [94]; see also [95]. It expands the Matlab SBToolbox with speci�c functionalities for power-law repr...

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“…This results in more advanced fitness functions, and is possible because evolutionary optimisation, unlike numerical methods, has the advantage of not restricting fitness function type. A similar method, (Spieth, Streichert, Speer & Zell, 2005b), uses the connectivity as a second objective in multi-objective optimisation. Analogously, Ando et al (2002);Iba (2008); Sakamoto & Iba (2001) have used genetic programming to evolve sparse ordinary differential equations, by penalising functions of large degree.…”

Section: Obtaining Skeletal/ Scale Free Structuresmentioning

confidence: 99%